Method and System for Output Presentation for Large Language Models and Agents

Abstract

Systems and methods for screening and scoring outputs generated by artificial intelligence language models, including receiving a generated output generated by an artificial intelligence language model in response to a user query, analyzing the output using detection engines, each detection engine evaluating different error dimensions, classifying each detected error into a severity level, generating color-coded visual indicators that correspond to a highest severity level among the one or more detected errors, generating remediation guidance configured to remediate at least one detected error of the one or more detected errors, and presenting screening results including the color-coded visual indicator a list of the one or more detected errors with their respective severity levels and locations, and the remediation guidance.

What is claimed is: 
     
         1 . A computer-implemented method for screening and scoring outputs generated by artificial intelligence language models, the method comprising:
 receiving, by a screening system, a generated output generated by an artificial intelligence language model in response to a user query;   analyzing the output using a plurality of detection engines, wherein each detection engine evaluates a different error dimension, detecting one or more detected errors;   for each detected error of the one or more detected errors, classifying the detected error into a severity level selected from a plurality of predefined severity levels;   generating a color-coded visual indicator that corresponds to a highest severity level among the one or more detected errors, wherein different colors represent different severity levels; and   generating remediation guidance configured to remediate at least one detected error of the one or more detected errors; and   presenting, to the user, screening results comprising:
 the color-coded visual indicator; 
 a list of the one or more detected errors with their respective severity levels and locations; and 
 the remediation guidance. 
   
     
     
         2 . The method of  claim 1 , wherein each detection engine of the plurality of detection engines evaluates a different error dimension selected from the group consisting of hallucination detection, factual accuracy verification, bias identification, security vulnerability detection, trustworthiness, explainability, code quality assessment, and performance analysis. 
     
     
         3 . The method of  claim 2 , wherein a detection engine of the plurality of detection engines evaluating for hallucination detection identifies fabricated information by at least one of:
 evaluating confidence scores associated with a metric of the generated output;   cross-referencing entities mentioned in the output against a knowledge graph; or   detecting logical contradictions within the output.   
     
     
         4 . The method of  claim 2 , wherein a detection engine of the plurality of detection engines evaluating for factual accuracy verification verifies claims by comparing content in the generated output against at least one authoritative knowledge base or database. 
     
     
         5 . The method of  claim 2 , wherein a detection engine of the plurality of detection engines evaluating for bias identification detects discriminatory patterns by at least one of:
 analyzing demographic parity across different subgroups mentioned in the output;   measuring sentiment disparity between different demographic groups; or   performing counterfactual testing by substituting protected attributes.   
     
     
         6 . The method of  claim 2 , further comprising identifying the generated output comprises code content; wherein a detection engine of the plurality of detection engines evaluating security vulnerability identifies exploitable weaknesses by at least one of:
 matching one or more code patterns comprised by the code content against known vulnerability signatures;   performing static code analysis on the code content; or   conducting data flow analysis on the code content to track potential security issues.   
     
     
         7 . The method of  claim 1 , wherein at least one detection engine of the plurality of detection engines employs a hybrid approach comprising:
 rule-based detection using predefined rules and patterns; and   machine learning-based detection using trained models.   
     
     
         8 . The method of  claim 1 , wherein classifying the detected error into a severity level comprises:
 assessing one or more potential consequences of the detected error on one or more of user decision-making or system operation;   adjusting the severity level based on an application domain associated with the generated output; and   applying a confidence threshold to filter out low-confidence detected errors.   
     
     
         9 . The method of  claim 1 , wherein the plurality of predefined severity levels comprises:
 a warning level indicating minor issues with limited impact;   an error level indicating significant issues requiring user attention; and   a critical level indicating severe issues that threaten output integrity or safety.   
     
     
         10 . The method of  claim 9 , wherein the color-coded visual indicator comprises:
 a green indicator when no errors are detected;   a yellow indicator when only warning level errors are detected;   an orange indicator when at least one error level error is detected without critical errors; and   a red indicator when at least one critical level error is detected.   
     
     
         11 . The method of  claim 1 , wherein the remediation guidance comprises:
 a description of a root cause of the detected error;   one or more suggested solutions for addressing the detected error; and   a reference to relevant documentation or best practices.   
     
     
         12 . The method of  claim 1 , further comprising, prior to classifying the one or more detected errors:
 aggregating the one or more detected errors from the plurality of detection engines;   removing duplicate detections of substantially similar errors comprised by the one or more detected errors; and   grouping related errors comprised by the one or more detected errors by location within the generated output.   
     
     
         13 . The method of  claim 1 , further comprising:
 determining that at least one detected error of the one or more detected errors requires expert validation based on at least one of:
 the error exceeding a predetermined severity threshold; or 
 the application domain being classified as high-stakes; 
   routing the generated output and the at least one detected error to a human expert for review; and   receiving validation or modification of the detected error from the human expert.   
     
     
         14 . The method of  claim 13 , further comprising incorporating feedback from the human expert into a training dataset for retraining at least one detection engine of the plurality of detection engines to improve detection accuracy. 
     
     
         15 . The method of  claim 1 , further comprising:
 receiving a modified version of the generated output from the user after the user implements changes based on the remediation guidance;   re-analyzing the modified version using the plurality of detection engines; and   confirming to the user when the modified version contains at least one of fewer errors or reduced severity compared to the generated output.   
     
     
         16 . The method of  claim 1 , further comprising storing, in an audit database, a record comprising:
 an identifier associated with the generated output;   each detected error of the one or more detected errors with its severity level, confidence score, and a timestamp indicating when the detected error was identified;   the color-coded visual indicator presented to the user; and   any user actions taken in response to the screening results.   
     
     
         17 . The method of  claim 1 , wherein the plurality of detection engines and the plurality of predefined severity levels are customized based on the application domain;
 and wherein the application domain is selected from financial services, healthcare, legal services, software development, or scientific research.   
     
     
         18 . The method of  claim 1 , wherein the plurality of detection engines is configured to operate each detection engine in parallel with each other detection engine of the plurality of detection engines. 
     
     
         19 . The method of  claim 1 , wherein the plurality of predefined severity levels is based on at least one of potential impact and context of use. 
     
     
         20 . A system for screening outputs generated by artificial intelligence language models, the system comprising:
 a processor;   a non-transitory computer-readable memory storing instructions; and   a plurality of detection engines stored in the memory,   wherein the instructions, when executed by the processor, cause the system to:
 receive an output generated by an artificial intelligence language model; 
 analyze the output using the plurality of detection engines operating in parallel, wherein each detection engine evaluates a different error dimension; 
 classify each detected error into a severity level based on potential impact and context; 
 generate a color-coded visual indicator corresponding to a highest severity level among detected errors; and 
 present screening results to a user, the screening results comprising the color-coded visual indicator, a list of detected errors with severity levels, and remediation guidance. 
   
     
     
         21 . The system of  claim 20 , further comprising:
 an input normalization module configured to standardize format of the received output;   an error aggregation module configured to consolidate detected errors from the plurality of detection engines;   a severity classification engine configured to assign severity levels based on predefined classification rules;   a visual encoding module configured to generate the color-coded visual indicator; and   a user interface module configured to display the screening results to the user.   
     
     
         22 . The system of  claim 20 , wherein the system comprises a distributed architecture including:
 an API gateway configured to receive screening requests;   a load balancer configured to distribute processing across multiple worker nodes;   a plurality of worker nodes, each worker node including at least a subset of the plurality of detection engines; and   a shared cache storing frequently accessed reference data for error detection.   
     
     
         23 . The system of  claim 20 , further comprising an application programming interface (API) configured to:
 receive outputs from an artificial intelligence platform before the outputs are delivered to end users;   perform the screening by executing the instructions stored in the memory; and   return the screening results to the artificial intelligence platform or directly to the end users based on configuration settings.